Exercise 2: Part 1: Calculate vulnerability index#

Aim of the exercise#

We want to create an overview of different vulnerability indicators. From the Covid-19 risk indicators dataset we take % permanent wall type, % permanent roof type and poverty incidence. From the Uganda population statistics we calculate the % of under fives and % of elderly. By combining the data, we are now able to visualize the areas in Uganda that are most vulnerable.

Data#

Download all datasets and save the folder on your computer and unzip the file. The zip folder includes:

Hint

All files still have their original names. However, feel free to modify their names if necessary to identify them more easily.

Task#

This first part of the exercise will prepare the data for subsequent non-spatial geodataprocessing, such as working with the attribute table. To calculate the vulnerability index, we will join all the relevant data using spatial geodataprocessing into a single vector layer.

  1. Load the Uganda district boundaries (admin level 2) (uga_admbnda_adm2_ubos_20200824.shp), as well as population statistics (uga_admpop_adm2_2020proj_1y.csv) and the Covid-19 risk indicators (COVID19_RISK_INDEX.shp) into QGIS.

  2. Make sure to reproject the dataset with the district boundaries and the dataset with the Covid-19 risk indicators into UTM zone 36N. Use the tool Reproject layer for this process. See the Wiki entry on projections for further information.

Attention

Before you start doing any GIS operations, always explore the data. Always check if the projections of the different layers are the same.

Hint

The projected coordinate system for Uganda is EPSG:32636 WGS 84 / UTM zone 36N. If you are looking for a suitable projected coordinate system for any region on earth, you can find a good one on epsg.io.

  1. We can see that the polygons are different in shape and amount! It is likely that the risk data is using an older version of the admin boundaries. This is an issue we need to resolve in order to work properly with the data.

../../_images/en_ex3_1_attribute_table_size.png

Fig. 178 Screenshot of different sizes of the attribute tables#

  1. We will use the following solution for this problem:

    • We can take the closest district centroid (from the dataset with the most to the dataset with the fewest records). This is the solution we will use for this exercise as the difference between the two datasets is not drastically.

  2. Calculate the Centroids for the dataset containing the most elements, which are the district boundaries. You can find the tool under Vector –> Geometry Tools –> Centroids. See the Wiki entry on Geoprocessing for further information.

  3. Edit the points so they are inside the correct polygons. This is necessary because the centroid of a polygon may fall outside of it when it has an unusual shape. To move a centroid that is outside its boundaries into the district boundaries, first activate the Toggle editing mode button, which can be found by clicking on while activating the centroid layer. Then, select the Move Feature tool. Search for the centroid that is outside its boundaries and move it to the appropriate district boundary. Save the changes and end the editing mode.

../../_images/en_centroids_screenshot_red.png

Fig. 179 The black points represent the centroids of the features of the input layer. The red circle indicates the centroid that requires editing.#

  1. There is an issue that can be found when joining the datasets, but it can be solved by using the Fix geometries tool on the Covid-19 risk dataset.

../../_images/en_ex3_1_fix_geometries.PNG

Fig. 180 Screenshot on how to fix the geometries.#

  1. Use the tool Join attributes by location to join the Covid-19 risk polygons onto the centroids. As a spatial relationship select within and select the columns %permrooft, %permwallt and Povertyinc as the fields that should be added. See the Wiki entry on spatial joins for further information.

../../_images/en_ex3_1_join_attribute_location_1.PNG

Fig. 181 Screenshot of Join attribute by location operation.#

  1. Use the tool Join attributes by location again to join the previously enriched points onto the Uganda district boundaries. Now select as a spatial relationship contain and again select the same three columns for joining.

../../_images/en_ex3_1_join_attribute_location_2.PNG

Fig. 182 Screenshot of the second Join attribute by location operation.#

The next steps of the vulnerability index calculation will be completed in the second part of this exercise, the Non-spatial Geodataprocessing section. Please refer to the provided link for this exercise.